3366results about "Other databases clustering/classification" patented technology

A computer network implemented system for improving the operation of one or more biofeedback computer systems is provided. The system includes an intelligent bio-signal processing system that is operable to: capture bio-signal data and in addition optionally non-bio-signal data; and analyze the bio-signal data and non-bio-signal data, if any, so as to: extract one or more features related to at least one individual interacting with the biofeedback computer system; classify the individual based on the features by establishing one or more brain wave interaction profiles for the individual for improving the interaction of the individual with the one or more biofeedback computer systems, and initiate the storage of the brain waive interaction profiles to a database; and access one or more machine learning components or processes for further improving the interaction of the individual with the one or more biofeedback computer systems by updating automatically the brain wave interaction profiles based on detecting one or more defined interactions between the individual and the one or more of the biofeedback computer systems. A number of additional system and computer implemented method features are also provided.

Systems and methods are disclosed for generating statistical models. Such systems and methods may utilize a database comprising data representing a plurality of variables. To generate a statistical model, a set of variables may be selected in accordance with a goal of the model. Using the database, the selected set of variables may then be applied to a plurality of statistical model types and the results from each statistical model type may be analyzed. Finally, at least one of statistical model may be identified based on the analysis of the results.

Information retrieval systems face challenging problems with delivering highly relevant and highly inclusive search results in response to a user's query. Contextual personalized information retrieval uses a set of integrated methodologies that can combine automatic concept extraction/matching from text, a powerful fuzzy search engine, and a collaborative user preference learning engine to provide accurate and personalized search results. The system can include constructing a search query to execute a search of a database parsing an input query from a user conducting the search of the database into sub-strings, and matching the sub-strings to concepts in a semantic concept network of a knowledge base. The system can further map the matched concepts to criteria and criteria values that specify a set of constraints on and scoring parameters for the matched concepts. Furthermore, the system can learn user preferences to construct one or more profiles for producing personalized search results.

A user interface enables the user to select a classification scheme from a group of classification schemes indicated by classification scheme information. The classification scheme information is stored in a classification-scheme storage unit as results of classification performed on data of arbitrary databases. The user interface also enables the user to select, from the group of databases, a database to which the selected classification scheme is applied. A classification module classifies the data of the selected database based on the selected classification scheme.

The present invention provides a hierarchical interface to an attribute based database. The method includes organizing a number of classifiers of data into a hierarchical structure and organizing the data into the hierarchical data structure according to the classifiers to which the data is linked. The hierarchical data structure can be a file system directory tree structure. The files system directory tree structure can be according to the NFS protocol. A request is received from a client, the request formatted according to the NFS protocol. The request is translated into a database query. A result is produced which is formatted according to the NFS protocol and the result is sent to the client. The present invention allows data to be presented in rearrange-able order based on a client view and classifiers, bound and unbound. The present invention also restricts access to files in a flexible manner.

A focused random walk system produces samples of on-topic pages from a collection of hyper-linked pages such as Web pages. The focused random walk system utilizes a focused random walk to produce a focused sample, which is a random sample of Web pages focused on a topic. The focused random walk system uniformly samples pages iteratively, where each iteration follows a random link from a union of the in-links and out-links of a page. The system then classifies this randomly selected link to determine whether the page is on-topic. The random walk sampling process could comprise a hard-focus method that selects only on-topic pages at each step of the focused random walk, or a soft-focus method that allows limited divergence to off-topic pages.

A system for clustering data objects includes a module for calculating an importance value of at least one member in a first data object represented as a variable length vector of 0 to N members and a clustering module for dynamically forming a plurality of clusters containing one or more data objects. The clustering module is configured to associate the first data object with at least one of the plurality of clusters in dependence upon the at least one member's similarity value in comparison to members in other data objects. The clustering module may be configured to cluster the first data object into a plurality of clusters if it has at least two members and each member belongs to a different cluster.

A fully or semi-automated, integrated learning, labeling and classification system and method have closed, self-sustaining pattern recognition, labeling and classification operation, wherein unclassified data sets are selected and converted to an assembly of graphic and text data forming compound data sets that are to be classified. By means of feature vectors, which can be automatically generated, a machine learning classifier is trained for improving the classification operation of the automated system during training as a measure of the classification performance if the automated labeling and classification system is applied to unlabeled and unclassified data sets, and wherein unclassified data sets are classified automatically by applying the machine learning classifier of the system to the compound data set of the unclassified data sets.

A method and system for site path evaluation using web session clustering is provided. The method identifies properties of a plurality of web site traversal paths. Data is acquired from a plurality of sessions corresponding to at least a portion of the plurality of web page traversal paths. Portions of the web site traversal paths are grouped into a unified web page category. The plurality of sessions is clustered into a plurality of web session clusters according to a similarity measure. One of the plurality of web session clusters most closely exhibiting at least one predefined metric is selected for analysis of the propertied of a web page traversal path contained therein. A system includes a plurality of web pages, a monitoring program and a computational process configured to receive data and identify properties of the plurality of traversal paths.

A technique for identifying related transaction records from a database storing transaction records for multiple entities includes grouping transaction records with a common attribute value into transaction record sets, receiving a selection of an exemplar record set and determining the probability the transaction record set stores transaction records associated with a first entity. Other operations include resolving the transaction record set as storing transaction records associated with the first entity. This improves the process of identifying related transaction records because related transaction records missed by string comparisons transaction record attributes are detected.

The invention discloses a fusion reasoning system and a fusion reasoning method for intelligent tags of news programs, relates to the technical field of news program tags. the intelligent news programtag identification system comprises an intelligent identification actuator, a historical tag library, an internal knowledge base, an internal case library and an analysis reasoner; the intelligent identification actuator executes identification tasks of various news program materials, and basic tag extraction is conducted on video images, voice and text information; Wherein the historical tag library stores materials, metadata and tags; The internal knowledge base is used for supplementing an intelligent recognition result and providing more information for subsequent analysis and reasoning;Wherein the internal case library is a case set established based on a historical tag library; According to the news program tag automatic fusion reasoning system and method, the intelligent recognition method is comprehensively utilized, the internal knowledge base and the internal case base are established based on the historical tag base, automatic fusion reasoning of news program tags is completed, and classification is accurate and efficient.

Systems and methods for clustering a group of data points based on a measure of similarity between each pair of data points in the group are provided. A pairwise similarity function can be estimated for each pair of data points in the group. A clustering algorithm can be executed to create clusters and associate data points with the clusters using the pairwise similarity function. The algorithm can be iterated multiple times until a stopping condition is reached in order to reduce variance in the output of the algorithm. The pairwise similarity function for each pair of data points can be updated between iterations of the algorithm and the results of each iteration can be aggregated. The data in each data point associated with a cluster can be consolidated into a consolidated data point.

A method and system facilitate a search of an information resource. The system identifies data items within the information resource and determines a respective attribute value from each data item. The system analyzes the attribute values to identify a distribution of the data items based on a predetermined range of attribute values that are associated with the first attribute and determines if the identified distribution of data items facilitates the search of the information resource.

The invention relates to an information pushing method and device, a computer readable storage medium and a computer device, and the method comprises the steps: obtaining a content score correspondingto to-be-pushed information, and the content score being determined according to the historical behavior features of a historical user corresponding to the to-be-pushed information; screening out candidate information from the to-be-pushed information according to the corresponding content score; determining an initial estimated click rate corresponding to the candidate information; fusing the exposure level determined based on the content score of the candidate information with the initial estimated click rate to obtain an estimated click rate corresponding to the candidate information; andwhen the estimated click rate meets a pushing condition, pushing candidate information corresponding to the estimated click rate to the new user. According to the scheme provided by the invention, thecontent quality of the information pushed to the new user and the high-quality information can be ensured to obtain more exposure opportunities.

A classification apparatus is provided. The classification apparatus includes a reader apparatus configured to receive visual information and textual information associated with the visual information and detect query relevant categorization information regarding products or service of interest to a user from the visual information and textual information associated with the visual information, a localize and identify apparatus configured to receive the visual information and the query relevant categorization information and selectively reduce the visual information to a relevant visual representation and detect further categorization information based on the relevant visual representation, and a deep learning processor apparatus comprising a unit classifier and a type classifier, wherein the unit classifier correlates the relevant visual representation with a specific product identification code, and the type classifier correlates the relevant visual representation with a type considered represented in the relevant visual representation.

In connection with the mining of time-evolving data streams, a general framework that mines changes and reconstructs models from a data stream with unlabeled instances or a limited number of labeled instances. In particular, there are defined herein statistical profiling methods that extend a classification tree in order to guess the percentage of drifts in the data stream without any labelled data. Exact error can be estimated by actively sampling a small number of true labels. If the estimated error is significantly higher than empirical expectations, there preferably re-sampled a small number of true labels to reconstruct the decision tree from the leaf node level.

The invention discloses a multi-modal emotion classification method based on text, voice and video fusion, and the method comprises the steps of obtaining the multi-modal data, preprocessing the multi-modal data, and dividing the multi-modal data into a training set and a test set; constructing an end-to-end multi-modal emotion classification model based on tensor fusion, and training the model by using the training set; and carrying out the preprocessing operation in the step 1 on the test set, and carrying out the sentiment classification by using the tensor fusion sentiment classification model obtained in the step 2. According to the present invention, the fuzzy deep emotion information can be better captured through the multi-modal emotion classification model.

The invention discloses a ship trajectory clustering analysis method based on an improved DBSCAN algorithm. The ship trajectory clustering analysis method comprises the following steps: S1, extractingeffective ship trajectory data from an AIS database; S2, carrying out trajectory similarity measurement by adopting a fusion distance MD, and obtaining a shortest super trajectory fused between trajectories; S3, calculating the length of the shortest super track by adopting the secondary time O (mn); S4, obtaining a fusion distance MD from the length of the track; S5, determining global parameters of the improved DBSCAN algorithm; S6, scanning the whole track segment data set through an improved DBSCAN algorithm to obtain a cluster set; and S7, for the obtained spatial motion mode, if a new track conforms to one of the ship operation modes, considering the track as a normal track. According to the ship trajectory clustering analysis method, the measurement precision is improved; and compared with a traditional DBSCAN algorithm, the time consumption is reduced while a multi-density data set can be well processed, and the robustness and adaptability are good, and tracks are correctly classified.

The invention discloses a multi-feature-fused matrix decomposition interest point recommendation method and an implementation system thereof. Firstly, according to the influence of friends and non-friends in the social relation on user decisions, the personalized distribution of user sign-in is calculated by using a self-adaptive bandwidth kernel density method in combination with user scores, andthe correlation between interest points is obtained; and then, because the sequence output by the Bi-LSTM has the characteristics of word semantics, syntax between the front and back of the word sequence and other hidden information, and the CNN is skilled to capture significant characteristics from a series of characteristics, the Bi-LSTM and the CNN are superposed to form a new deep neural network, thereby learning the potential characteristics of the user and the interest point. Finally, the social contact, the geographic position, the classification preference and the potential features are fused through a probability matrix method, the personalized preference of the user is predicted, and therefore the purpose of personalized recommendation is achieved.

The invention relates to the technical field of archive management, particularly relates to an archive management system and an operation process. The archive management system comprises a user queryterminal, an information collection platform, an archive processing system, an archive storage terminal and an automatic compression backup unit; the archive arrangement and filing unit comprises an archive data classification standard unit, an archive data identification unit and an archive data description unit, and the archive processing system comprises an archive retrieval module, an archivemanagement module, a retrieval service module, a system configuration module and a file configuration module. According to the scheme of the invention, archive information input by the information collection platform can be quickly sorted and stored in a classified manner; a user can conveniently input corresponding retrieval conditions in an operation interface to export corresponding archive data, the user with corresponding permission can edit, arrange and perform other related operations on archives, irrelevant personnel are prevented from forcibly changing archive information, informationleakage is prevented, the safety of the archives is guaranteed, and the archive arrangement and retrieval efficiency of the user is improved.

This invention is generally directed to one or more methods or systems relating to inferential networks. This invention is further directed to methods and systems for making and using inferential networks. Still further, this invention is generally directed to making one or more inferential networks by using data generated from directed searches of collections of linked databases.

The invention relates to an invoice collection management system and method based on an OCR image recognition technology. An invoice information acquisition unit of the system acquires invoice pictureinformation data; the OCR image identification unit identifies and reads the invoice image information data based on an OCR image identification technology in combination with an artificial intelligence deep learning algorithm to obtain an OCR image identification result; the invoice information verification processing unit identifies the invoice type of the OCR image identification result and automatically calls a tax bureau value-added tax invoice authenticity checking platform to carry out authenticity checking when the identification result is the value-added tax invoice; the invoice information storage management unit stores an OCR image recognition result of which the identification result is a non-value-added tax invoice, repeatedly collects and checks the OCR image recognition result of the value-added tax invoice of which the check result is true, and then correspondingly stores the OCR image recognition result; and the invoice information classification display unit carriesout classification display processing on the invoice information data and comprehensively realizes the whole process of collection, identification, checking, processing and storage of the invoice information data.

The invention provides an information recommendation method and device based on artificial intelligence, electronic equipment and a computer readable storage medium. The method comprises the steps ofobtaining a historical behavior information sequence of a user; according to the vector representation of each piece of information in the historical behavior information sequence, determining a behavior feature vector representing the behavior feature of the user; iteratively encoding the behavior feature vector for multiple times to obtain a user interest vector representing the interest of theuser; and determining a plurality of pieces of information satisfying similar conditions with the user interest vector to execute recommendation operation for the user. According to the invention, therecommendation accuracy and the click rate index of the recommendation information can be improved.

The invention provides a dressing recommendation method and apparatus. The method comprises the steps of obtaining a first eigenvector of a to-be-recommended garment; inputting the first eigenvector to a machine learning model for performing learning to obtain a target attribute of the to-be-recommended garment; and according to current weather information and the target attribute of the to-be-recommended garment, forming a target recommended garment for a user. Through the method, the garment accordant with a current weather condition can be intelligently recommended to the user, so that the problems of long consumed time, wrong dressing and the like due to the fact that the existing user selects out the garment suitable for the weather condition according to own experience are solved.